This disclosure is directed to computers, and computer applications for identifying crop health, and more particularly to computer-implemented methods and systems for generating a map identifying the size and location of anomalous crop health patterns of a geographic area.
Acquisition of geo-registered airborne imagery from various platforms with the goal of assessing crop health and/or crop growth or yield is known. There are several known approaches and tools developed to acquire and analyze crop health. However, the known techniques use some form of an empirical approach to utilize the results of the analysis to determine where crop health/growth is degraded and where it is not. Some current systems are focused on integrating the data, displaying the imagery and letting the determination of areas of anomalous crop health, whether good or bad, to be made by hand using the farmer's experience and expert judgment.
Examples of such current systems are Decision Support System for Agrotechnology Transfer (DSSAT) and Daisy. The crop simulation models in DSSAT simulate growth, development and yield as a function of the soil-plant-atmosphere dynamics. Daisy is a soil-plant-atmosphere system model designed to simulate water balance, heat balance, solute balance and crop production in agro-ecosystems subjected to various management strategies. DSSAT and Daisy employ mechanistic models and algorithms to forecast crop growth, yields and crop health.
Some current systems use color range in the imagery to present a qualitative assessment of anomalous crop health, which can often be misleading. There is no way in the current systems to separate what are real anomalies from regions of high/low crop health/growth that are still within expected or normal bounds. In addition, the current systems lack predictive modeling to understand expected behavior at times in the future
Some known vegetation classification approaches rely on manual identification of a reference location or plot of healthy vegetation. In one known image analysis system, red appears to indicate areas of poor crop health and green is acceptable, but typically there is no color scale defining the values on the image and the actual difference between red and green is unknown.